Project Part 1

Group 9

Authors

Affiliation

Thimoté Dupuch

University of Twente

Joris van Lierop

University of Twente

Jurre van Sijpveld

University of Twente

Loading libraries

library(dplyr)
library(forcats)
library(vtable)
library(ggplot2)
library(plotly) # Optional

Loading dataset

dataset <- read.csv("SMARTc.csv", sep = ";") # Without missing values

Re-encode the categorical variables

dataset <- mutate(dataset,
  EVENT = factor(EVENT),
  EVENT = fct_recode(EVENT, "no" = "0", "yes" = "1"),

  SEX = factor(SEX),
  SEX = fct_recode(SEX, "male" = "1", "female" = "2"),

  DIABETES = factor(DIABETES),
  DIABETES = fct_recode(DIABETES, "no" = "0", "yes" = "1"),

  SMOKING = factor(SMOKING),
  SMOKING = fct_recode(SMOKING, "never" = "1", "former" = "2", "current" = "3"),

  alcohol = factor(alcohol),
  alcohol = fct_recode(alcohol, "never" = "1", "former" = "2", "current" = "3"),

  CEREBRAL = factor(CEREBRAL),
  CEREBRAL = fct_recode(CEREBRAL, "no" = "0", "yes" = "1"),

  CARDIAC = factor(CARDIAC),
  CARDIAC = fct_recode(CARDIAC, "no" = "0", "yes" = "1"),

  AAA = factor(AAA),
  AAA = fct_recode(AAA, "no" = "0", "yes" = "1"),

  PERIPH = factor(PERIPH),
  PERIPH = fct_recode(PERIPH, "no" = "0", "yes" = "1"),

  albumin = factor(albumin),
  albumin = fct_recode(albumin, "no" = "1", "micro" = "2", "macro" = "3"),

  STENOSIS = factor(STENOSIS),
  STENOSIS = fct_recode(STENOSIS, "no" = "0", "yes" = "1"),

)

Description of the dataset and table of variables

The dataset is about cardiovascular health. It contains two outcomes : EVENT and TEVENT, the presence of cardiovascular events and the number of days the patient is in study until the event occurs. The dataset contains many variables, some of them are categorical and some of them are numerical. It covers patient descriptives, classical risk factors, previous symptomatic atherosclerosis, and markers of atherosclerosis.

sumtable(dataset, out = "return", add.median = TRUE)

Association between variables and the outcome

avg_event_proportion <- mean(as.numeric(dataset$EVENT == "yes"))
SMOKING <- dataset$SMOKING
EVENT <- dataset$EVENT

bar_plot <- ggplot(dataset, aes(x = SMOKING, fill = EVENT)) +
    geom_bar(position = "fill") + # "fill" scales the bars to proportion
    geom_hline(yintercept = avg_event_proportion, linetype = "dashed") +
    labs(
        title = "Cardiovascular Event by Smoking Status",
        x = "Smoking Status", y = "Proportion (-- : Average)",
        fill = "Cardiovascular Event"
    )

ggplotly(bar_plot,width = 500, height = 600)